The DOI matches its Crossref record; 6 of the 7 references it cites were found in a registry or an index.
Small studies: strengths and limitations
Open accessAbstract
A large number of clinical research studies are conducted, including audits of patient data, observational studies, clinical trials and those based on laboratory analyses. While small studies can be published over a short time-frame, there needs to be a balance between those that can be performed quickly and those that should be based on more subjects and hence may take several years to complete. The present article provides an overview of the main considerations associated with small studies. The definition of “small” depends on the main study objective. When simply describing the characteristics of a single group of subjects, for example the prevalence of smoking, the larger the study the more reliable the results. The main results should have 95% confidence intervals (CI), and the width of these depend directly on the sample size: large studies produce narrow intervals and, therefore, more precise results. A study of 20 subjects, for example, is likely to be too small for most investigations. For example, imagine that the proportion of smokers among a particular group of 20 individuals is 25%. The associated 95% CI is 9–49. This means that the true prevalence in these subjects generally is anywhere between a low or high value, which is not a useful result. When comparing characteristics between two or more groups of subjects ( e.g. examining risk factors or treatments for disease), the size of the study depends on the magnitude of the expected effect size, which is usually quantified by a relative risk, odds ratio, absolute risk difference, hazard ratio, or difference between two means or medians. The smaller the true-effect size, the larger the study needs to be 1, 2. This is because it is more difficult to distinguish between a real effect and random variation. Consider mortality as the end-point in a …
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“Studies of intervention and control group population sample sizes (N) < 10 were excluded to reduce the likelihood of small study effects, for example over-estimation of effect size [33].”
From Estimating the Effect of Aerobic Exercise Training on Novel Lipid Biomarkers: A Systematic Review and Multivariate Meta-Analysis of Randomized Controlled Trials (2023) · cites this paper as [33]
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“This fact was confirmed in 2021 by the European Institute for Gender Equality [67], which shows only 30.7% of women on Ibex-35 companies' Board of Directors.”
From Pilot study of an online brief mindfulness-based intervention to improve the well-being of managers and reduce their stress at work. (2024) · cites this paper as [67]
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Cites
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Sample Size Tables for Clinical Studies
Machin, D., Campbell, M. J., Tan, S. B., & Tan, S. H. (2008) · Wiley eBooks · doi:10.1002/9781444300710 · Cited by 1,336
Clinical Trials a Practical Approach
Pocock, S. J. (2013) · Thammasat University Digital Collections · Cited by 213
Lee, S. M., James, L. E., Büchler, T., Snee, M. P., Ellis, P., & Hackshaw, A. K. (2007) · Lung Cancer · doi:10.1016/j.lungcan.2007.08.032 · Cited by 36
Self-citationLee, S. M., Rudd, R., Woll, P. J., Ottensmeier, C. H. H., James, L. E., Gower, N. H., Spiro, S. G., Jitlal, M., & Hackshaw, A. K. (2008) · Journal of Clinical Oncology · doi:10.1200/jco.2008.26.15_suppl.8045 · Cited by 12
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The global prevalence of rheumatoid arthritis: a meta-analysis based on a systematic review
Almutairi, K. B., Nossent, J. C., Preen, D. B., Keen, H. I., & Inderjeeth, C. A. (2020) · Rheumatology International · doi:10.1007/s00296-020-04731-0 · Cited by 810
Wang, L., Hong, P. J., May, C., Rehman, Y., Oparin, Y., Hong, C. J. H., Hong, B. Y., AminiLari, M., Gallo, L., Kaushal, A., et al. (2021) · BMJ · doi:10.1136/bmj.n1034 · Cited by 318
Wallis, C. J. D., Butaney, M., Satkunasivam, R., Freedland, S. J., Patel, S. P., Hamid, O., Pal, S. K., & Klaassen, Z. W. A. (2019) · JAMA Oncology · doi:10.1001/jamaoncol.2018.5904 · Cited by 294
Ahmad, R., Riaz, M., Khan, A., Aljamea, A., Algheryafi, M., Sewaket, D., & Alqathama, A. A. (2021) · Phytotherapy Research · doi:10.1002/ptr.7215 · Cited by 183
“Yes, indeed, such kinds of studies (enrolling patients, reviewing patient records, performing biochemical analyses/asking subjects to complete study questionnaires) are help- ful, and data produced thereof may be utilised to design confirmatory studies on a large scale (Hackshaw, 2008).”
BackgroundCortez, J. M., Gupta, M., Amaram, A., Pizzino, J., Sawhney, M., & Sood, B. G. (2010) · The Journal of Maternal-Fetal & Neonatal Medicine · doi:10.3109/14767058.2010.511335 · Cited by 164
“Such studies are often performed in a small number of subjects initially [26].”
Background
Metadata
- Authors
- Hackshaw, A.
- Affiliations
- Cancer Research UK (GB)
- Corresponding author
- Hackshaw, A.
- ORCID
- Hackshaw, A.
- Venue
- European Respiratory Journal
- Publisher
- European Respiratory Society (ERS)
- Published
- 2008-10-31
- Volume
- 32
- Issue
- 5
- Pages
- 1141-1143
- ISSN
- 0903-1936 (ISSN-L)
- 1399-3003
- OpenAlex
- W2097412619
- Cited by
- 496
- References
- 7
- Citations by year
- 2026: 29 · 2025: 36 · 2024: 37 · 2023: 56 · 2022: 57 · 2021: 86 · 2020: 51 · 2019: 38 · 2018: 24 · 2017: 20 · 2016: 20 · 2015: 17 · 2014: 9 · 2013: 5 · 2012: 5
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- European Respiratory Journal (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- small sample size, sample size determination, confidence intervals, statistical power, effect size, clinical research design, research limitations, prevalence estimation
- MeSH terms
- Antineoplastic Agents, Clinical Trials as Topic, Data Interpretation, Statistical, Diet Therapy, Humans, Lung Neoplasms, Research Design, Risk Factors, Smoking, Thalidomide, Time Factors, Reproducibility of Results, Weight Loss, Sample Size, Small Cell Lung Carcinoma
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